the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of different solar EUV proxies and Ap index on hmF2 trend analysis
Abstract. Long-term trend estimation in the peak height of the F2 layer, hmF2, needs the previous filtering of much stronger natural variations such as those linked to the diurnal, seasonal, and solar activity cycles. If not filtered, they need to be included in the model used to estimate the trend. The same happens with the maximum ionospheric electron density that occurs in this layer, NmF2, usually analyzed through the F2 layer critical frequency, foF2. While diurnal and seasonal variations can be easily managed, filtering the effects of solar activity presents more challenges, as does the influence of geomagnetic activity. However, recent decades have shown that geomagnetic activity may not significantly impact trend assessments. On the other hand, the choice of solar activity proxies for filtering has been shown to influence trend values in foF2, potentially altering even the trend's sign. This study examines the impact of different solar activity proxies on hmF2 trend estimations, using data updated to 2022, including the ascending phase of solar cycle 25, and explores the effect of including the Ap index as a filtering factor. The results obtained, based on two mid-latitude stations, are also comparatively analyzed to those obtained for foF2. The main findings indicate that the squared correlation coefficient, r2, between hmF2 and solar proxies, regardless of the model used or the inclusion of the Ap index, is consistently lower than in the corresponding foF2 cases. This lower r2 value in hmF2 suggests a greater amount of unexplained variance, indicating that there is significant room for improvement in these models. However, in terms of trend values, foF2 shows greater variability depending on the proxy used, whereas the inclusion or exclusion of the Ap index does not significantly affect these trends. This suggests that foF2 trends are more sensitive to the choice of solar activity proxy. In contrast, hmF2 trends, while generally negative, exhibit greater stability than foF2 trends.
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Status: open (until 04 Oct 2024)
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RC1: 'Comment on egusphere-2024-2479', Anonymous Referee #1, 31 Aug 2024
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Reviewer comments to the paper by Duran et al.
The paper describes an approach to looking for the hmF2 trends. More or less similar job has been described for the foF2 trends in the previous work of the same group. The principal introducing point of the authors is that there is a need for “…the correct filtering of the solar and geomagnetic influence on the data…” in looking for trends in foF2 and hmF2. They state that “…the problem is not yet fully resolved and deserves to be further and more deeply investigated and expanded.”
In the Introduction, the authors briefly describe some previous approaches to looking for the hmF2 trends and present their concept of the problems related to that task.
The ionospheric data used for the analysis are presented in Section 2. The observations at Rome and Juliusruh stations are analyzed. The foF2 and M(3000) parameters are used to calculate hmF2 by Shimazaki formula.
To get rid of SA effects in the hmF2 changes, the authors consider usual set of five solar proxies: F10.7, SN, Ly-a, MgII, and F30. The difference in their approach is that they consider also possible effects of changes in geomagnetic activity.
The authors consider a reliability of the Shimazaki formula. They conclude that it works well at night but overestimates hmF2 in the daytime. However, in terms of trends, the hmF2 values by Shimazaki formula work well, this fact being illustrated visually by Fig. 2.
Various kinds of regression equation used to calculate the hmF2 trend are described in Section 4.
To find effects of each solar proxy and geomagnetic activity on the process that the authors call “filtering”, the authors analyze the squared correlation coefficient r2 for all four models and all five solar proxies together with the trend values obtained in each case
The consideration of the r2 values for hmF2 and foF2 over three periods 1960-2022, 1976-2014, and 1976-2022 (Figs. 3-6) shows that inclusion of more complicated regressions (especially with allowance for Ap index) increases the r2 values for hmF2. However, it is not so for foF2.
I think that the most interesting results are presented in Figs. 7-10. They show the hmF2 and foF2 trends for each of three aforementioned periods with “filtering” by various SA proxies. An important point is that in all cases the trends for the longest period of 1960-1922 are the lowest. In my opinion, it is one more proof that trends of anthropogenic origin have begun to appear somewhere after 1980.
The results in r2 and trends for different approaches to the “filtering” by three SA proxies (F10.7, MgII, and Ly-a) as compared to F30 are shown in Figs. 11-18. The results of using more complicated regression than the simplest one show that the r2 differences are substantially greater for hmF2 than for foF2 in all cases. It is an important result demonstrating that the hmF2 trends are more sensitive to inclusion of Ap effects more complicating regressions in the “filtering’ than the foF2 trends.
I consider the paper as a very interesting step helping substantially in solving the great problem of deriving long-term trends in parameters of the F2 layer. I recommend publication of the paper as it is. My only small comment is that there are no units of the trends at the ordinates of figs 7-10. I assume that they are km/year and MHz/year, but it should be indicated in the plots or captions.
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